AI-Powered GPS Systems for Vehicle Density Control: A Review of Intelligent Speed Regulation Methods and Their Complications: A Literature Review

The rising issues regarding road safety, dynamic speed regulation, and traffic jams in cities may be resolved with the use of GPS and artificial intelligence (AI) integrated into transportation systems. This review examines the potential and implementation difficulties of AI-enhanced GPS-based vehicle density management systems. The focus is on a system that connects every car with a GPS-enabled interface so that real-time information like location, speed, and direction can be communicated. In order to classify different types of roads such as highways, expressways, pocket roads and sensitive locations (such as city areas, hospitals, and schools), this data is processed through a central server that is integrated with digital road maps like OpenStreetMap (OSM). Using GPS and speed data, the system determines safe braking distances and evaluates vehicle density within a 300-meter radius. The safe speed limit is dynamically determined by a rule-based or machine learning engine that takes factors such as zone sensitivity, vehicle density and route categorization. An Engine Control Unit (ECU) either provides the driver with the resulting speed recommendations or applies them. The potential of AI-driven GPS systems to lower accident rates and improve roadway performance is demonstrated by this flexible and context-aware methodology. The study summarizes current research, points out knowledge gaps, and suggests potential directions for intelligent, scalable traffic control systems.